Effects of restricting environmental range of data to project current and future species distributions

We examine the consequences of restricting the range of environmental conditions over which niche-based models are developed to project potential future distributions of three selected European tree species to assess first, the importance of removing absences beyond species known distributions (naughty noughts) and second the importance of capturing the full environmental range of species. We found that restricting the environmental range of data strongly influenced the estimation of response curves, especially towards upper and lower ends of environmental ranges. This induces changes in the probability values towards upper and lower environmental boundaries, leading to more conservative scenarios in terms of changes in distribution projections.
Using restricted data analogous to not capturing the fun species' environmental range, reduces strongly the combinations of environmental conditions under which the models are calibrated, and reduces the applicability of the models for predictive purposes. This may generate unpredictable effects on the tails of the species response curves, yielding spurious projections into the future provided that probability of occurrence is not set to zero outside the environmental limits of the species. Indeed, as the restricted data does not capture the whole of the response curve, projections of future species distributions based of ecological niche modelling may be only valid if niche models are able to approach the complete response curve of environmental predictors.     

Linking environmental and demographic data to predict future population viability of a perennial herb

Recent advances in stochastic demography provide tools to examine the importance of random and periodic variation in vital rates for population dynamics. In this study, we explore with simulations the effect of disturbance regime on population dynamics and viability. We collected 7 years of demographic data in three populations of the perennial herb Primula farinosa, and used these data to examine how variation in vital rates affected population viability parameters (stochastic growth rate, λ_S), and how vital rates were related to weather conditions. Elasticity analysis indicated that the stochastic growth rate was very sensitive to changes in regeneration, quantified as the production, survival, and germination of seeds. In one of the study years, all seedlings and mature plants in the demography plots died. This extinction coincided with the driest summer during the study period. Simulations suggested that a future increase in the frequency of high-mortality years due to climate change would result in reduced population growth rate, and an increased importance of survival in the seed bank for population viability. The results illustrate how the limited demographic data typically available for many natural systems can be used in simulation models to assess how environmental change will affect population viability.     

Linking palaeoenvironmental data and models to understand the past and to predict the future

Complex, process-based dynamic models are used to attempt to mimic the intrinsic variability of the natural environment, ecosystem functioning and, ultimately, to predict future change. Palaeoecological data provide the means for understanding past ecosystem change and are the main source of information for validating long-term model behaviour. As global ecosystems become increasingly stressed by, for example, climate change, human activities and invasive species, there is an even greater need to learn from the past and to strengthen links between models and palaeoecological data. Using examples from terrestrial and aquatic ecosystems, we suggest that better interactions between modellers and palaeoecologists can help understand the complexity of past changes. With increased synergy between the two approaches, there will be a better understanding of past and present environmental change and, hence, an improvement in our ability to predict future changes.     

Fish as models for environmental genomics

Fish offer important advantages for defining the organism-environment interface and responses to natural or anthropogenic stressors. Genomic approaches using fish promise increased investigative power, and have already provided insights into the mechanisms that underlie short-term and long-term environmental adaptations. The range of fish species for which genomic resources are available is increasing, but will require significant further expansion for the optimal application of fish environmental genomics.     

A new look at state-space models for neural data

State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.     

Temperature data for phenological models

In an arid environment, the effect of evaporation on energy balance can affect air temperature recordings and greatly impact on degree-day calculations. This is an important consideration when choosing a site or climate data for phenological models. To our knowledge, there is no literature showing the effect of the underlying surface and its fetch around a weather station on degree-day accumulations. In this paper, we present data to show that this is a serious consideration, and it can lead to dubious models. Microscale measurements of temperature and energy balance are presented to explain why the differences occur. For example, the effect of fetch of irrigated grass and wetting of bare soil around a weather station on diurnal temperature are reported. A 43-day experiment showed that temperature measured on the upwind edge of an irrigated grass area averaged 4% higher than temperatures recorded 200 m inside the grass field. When the single-triangle method was used with a 10°C threshold and starting on May 19, the station on the upwind edge recorded 900 degree-days on June 28, whereas the interior station recorded 900 degree-days on July 1. Clearly, a difference in fetch can lead to big errors for large degree-day accumulations. Immediately after wetting, the temperature over a wet soil surface was similar to that measured over grass. However, the temperature over the soil increased more than that over the grass as the soil surface dried. Therefore, the observed difference between temperatures measured over bare soil and those over grass increases with longer periods between wettings. In most arid locations, measuring temperature over irrigated grass gives a lower mean annual temperature, resulting in lower annual cumulative degree-day values. This was verified by comparing measurements over grass with those over bare soil at several weather stations in a range of climates. To eliminate the effect of rainfall frequency, using temperature data collected only over irrigated grass, is recommended for long-term assessment of climate change effects on degree-day accumulation. In high evaporative conditions, a fetch of at least 100 m of grass is recommended. Our results clearly indicate that weather stations sited over bare soil have consistently higher degree-day accumulations. Therefore, especially in arid environments, phenology models based on temperature collected over bare soil are not transferable to those based on temperature recorded over irrigated grass. At a minimum, all degree-day-based phenology models reported in the literature should clearly describe the weather station site. Received: 25 October 2000 / Revised: 10 July 2001 / Accepted: 10 July 2001     

Amphibians as models for studying environmental change

The use of amphibians as models in ecological research has a rich history. From an early foundation in studies of amphibian natural history sprang generations of scientists who used amphibians as models to address fundamental questions in population and community ecology. More recently, in the wake of an environment that human disturbances rapidly altered, ecologists have adopted amphibians as models for studying applied ecological issues such as habitat loss, pollution, disease, and global climate change. Some of the characteristics of amphibians that make them useful models for studying these environmental problems are highlighted, including their trophic importance, environmental sensitivity, research tractability, and impending extinction. The article provides specific examples from the recent literature to illustrate how studies on amphibians have been instrumental in guiding scientific thought on a broad scale. Included are examples of how amphibian research has transformed scientific disciplines, generated new theories about global health, called into question widely accepted scientific paradigms, and raised awareness in the general public that our daily actions may have widespread repercussions. In addition, studies on amphibian declines have provided insight into the complexity in which multiple independent factors may interact with one another to produce catastrophic and sometimes unpredictable effects. Because of the complexity of these problems, amphibian ecologists have been among the strongest advocates for interdisciplinary research. Future studies of amphibians will be important not only for their conservation but also for the conservation of other species, critical habitats, and entire ecosystems.     

A comparison of three maximum likelihood models for stage-frequency data

A comparison has been made between the estimates obtained from maximum likelihood estimation of gamma, inverse normal, and normal distribution models for stage-frequency data. Results have been compared for six of sets of test data, and from many sets of simulated data. It is concluded that (1) some estimates may differ substantially between the models, (2) estimates from the correct model have little bias, and estimated standard errors are generally close to theoretical values, (3) there are problems in determining degrees of freedom for chi-squared goodness of fit tests, so that it is best to compare test statistics with simulated distributions, and (4) goodness of fit tests may not discriminate well between the three models.     

History and future of animal welfare science

It is suggested that Mench give credit to the books that started the farm animal welfare movement--Harrison's (1964) Animal Machines and, arriving 1 year later, Huxley's (1965) Brave New Victuals. Huxley covered the use of chemicals on the land and “factory fanning, ”a phrase coined by Harrison. They both discussed the rearing of animals indoors in huge numbers, closely confined, artificially lit, mechanically supplied with food, and treated with drugs and hormones to prevent disease and to speed growth. Are intensive methods necessarily cruel to animals? Are agricultural and industrial chemical residues contaminating the environment? What is the influence of new methods on the quality of food and what are the effects on the human organism? These were relevant questions back then, and almost 30 years later Harrison (1988, 1993) wondered how much real progress has been made in answering them.     

Sustaining biological welfare for our future through consistent science

Physiological anthropology presently covers a very broad range of human knowledge and engineering technologies. This study reviews scientific inconsistencies within a variety of areas: sitting posture; negative air ions; oxygen inhalation; alpha brain waves induced by music and ultrasound; 1/f fluctuations; the evaluation of feelings using surface electroencephalography; Kansei; universal design; and anti-stress issues. We found that the inconsistencies within these areas indicate the importance of integrative thinking and the need to maintain the perspective on the biological benefit to humanity. Analytical science divides human physiological functions into discrete details, although individuals comprise a unified collection of whole-body functions. Such disparate considerations contribute to the misunderstanding of physiological functions and the misevaluation of positive and negative values for humankind. Research related to human health will, in future, depend on the concept of maintaining physiological functions based on consistent science and on sustaining human health to maintain biological welfare in future generations.     

A comparison of probe-level and probeset models for small-sample gene expression data

Background  

Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.